Job Description
hackajob is collaborating with Baringa Partners to connect them with exceptional professionals for this role.
Apply now, read the job details by scrolling down Double check you have the necessary skills before sending an application.
What you will be doing
- Defining and implementing Machine Learning projects over the full lifecycle, from conception to data preparation, model engineering, evaluation and deployment and finally model monitoring and maintenance
Establishing and developing ML Ops frameworks and standards for clients and embedding within their infrastructureWorking with clients to take them on the journey, upskilling along the way and ensuring they are kept in the loop and can take ownership after you roll off the projectPerforming maturity assessments across clients' Cloud/AI environments and recommending improvementsBuilding ML strategy blueprints and advising clients on the different technology optionsTranslating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisations strategy, policies and standards and in some cases, help customers to define new policies, philosophies and standardsHelping clients to identify risks and mitigations for their ML and DS programmes, as well as transition from on-prem to modern cloud-based infrastructures (AWS, Azure, GCP)Working with clients in key areas of ML model governance, such as in defining philosophies including fairness, transparency, interpretability, and accountability
Your skills and experience
We are seeking passionate and dynamic ML engineers who are excited by building production ML solutions, and keen to take an active part in the growth of the company. xxuwjjq We're looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life.
- Passionate person who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements
Advanced degree in computer science, mathematics, physics, engineering or related STEM fieldStrong problem-solving skills and solid grounding in classical ML and deep learning: from applied statistics and traditional machine learning algorithms to transformers and SOTA deep learningExcellent collaboration and communication skills, both with teams and in client-facing engagementsInterested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agentsProven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environmentsAbility to design, deploy and maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API designHands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning studio)Be a 'lifelong learner' and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you